Abstract: Much current work on knowledge acquisition for intelligent systems concentrates on the use of predifined models of problem-solving methods to define the roles in which domain knowledge is used to solve particular application tasks. Knowledge-acquisition tools that incorporate such models impose task-specific architectures on the knowledge bases that the tools are used to construct. PROTÉGÉ-I is a metalevel program that generates knowledge-acquisition tools tailored for classes of application tasks. PROTÉGÉ-I includes a model of problem solving via the method of episodic skeletal-plan refinement (ESPR). Knowledge engineers extend this model of problem solving with domain knowledge to define models of relevant application areas. Current research in our laboratory concerns a new architecture, PROTÉGÉ-II, in which knowledge engineers assemble the ESPR model using a library of smaller buidling blocks, called problem-solving mechanisms. In addition to providing flexibility in the definition of the control strategy for the expert systems that PROTÉGÉ-II ultimately generates, the new architecute allows knowledge engineers to represent static domain knowledge as explicit ontologies of concepts and relationships that may themselves be reusable.